Efficient Variable Rate Image Compression With Multi-Scale Decomposition Network

While deep learning image compression methods have shown an impressive coding performance, most of them output a single-optimized-compression rate using a trained-specific network. However, in practice, it is essential to support the variable rate compression or meet a target rate with a high-coding performance. This paper proposes a novel image compression method, making it possible for a single convolutional neural network (CNN) model to generate the variable rate efficiently with an optimized rate-distortion (RD) performance. The method consists of CNN-based multi-scale decomposition transform and content adaptive rate allocation. Specifically, the transform network is learned to decompose the input image into several scales of representations while optimizing the RD performance for all scales. Rate allocation algorithms for two typical scenarios are provided to determine the optimal scale of each image block for a given target rate or quality factor. For a target rate, the allocation is adaptive based on content complexity. In addition, for a target quality factor which indicates a tradeoff between the rate and the quality, the optimal scale is determined by minimizing the RD cost. The experimental results have shown that our method has outperformed the JPEG2000 and BPG standards with high efficiency and the state-of-the-art RD performance as measured by the multi-scale structural similarity index metric. Moreover, our method can strictly control the rate to generate the target compression result.